Encrypted network traffic identification method based on deep neural network

Abstract

The invention relates to an encrypted network traffic identification method based on a deep neural network, and belongs to the technical field of deep learning, network service security and traffic identification. The encrypted network traffic identification method based on the deep neural network comprises the steps of 1, obtaining an offline data set based on capture, deployment and extraction operations, and generating a training set and a test set; 2, building a deep neural network model; 3, performing data reading, model training and parameter optimization: inputting the offline data set into a deep neural network model for training and iteration until the accuracy reaches the standard, and then stopping training; 4, establishing and deploying an online network flow capture platform, and capturing an online data set; and 5, performing online network flow identification to obtain an identification result. According to the method, high-dimensional features of the flow data can be better extracted; compared with an existing deep neural network, the method has the advantages of better multi-classification recognition accuracy, lower false positive rate and lower false alarm rate, and ensures the high efficiency of encrypted data flow on-line recognition.

Description

technical field [0001] The invention relates to a method for identifying encrypted network traffic based on a deep neural network, which aims at identifying encrypted network traffic types, and belongs to the technical fields of deep learning, network service security, and traffic identification. Background technique [0002] Traffic is an important carrier of various types of information in network transmission. In order to protect user privacy, existing networks mostly use the SSL / TLS encryption protocol to encrypt network traffic. Through the analysis and identification of encrypted network traffic, it can provide technical support for the traffic audit work of network service providers, so that they can better formulate routing strategies, improve the data distribution efficiency of key transmission nodes, provide a theoretical basis, and further improve the user experience of network users. experience. In practical application of this method, it should be arranged in ...

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